| Robust stochastic gradient descent with student-t distribution based first-order momentum WEL Ilboudo, T Kobayashi, K Sugimoto IEEE Transactions on Neural Networks and Learning Systems 33 (3), 1324-1337, 2020 | 50 | 2020 |
| T-soft update of target network for deep reinforcement learning T Kobayashi, WEL Ilboudo Neural Networks 136, 63-71, 2021 | 40 | 2021 |
| Student-t policy in reinforcement learning to acquire global optimum of robot control T Kobayashi Applied Intelligence 49 (12), 4335-4347, 2019 | 35 | 2019 |
| Unified bipedal gait for autonomous transition between walking and running in pursuit of energy minimization T Kobayashi, K Sekiyama, Y Hasegawa, T Aoyama, T Fukuda Robotics and Autonomous Systems 103, 27-41, 2018 | 25 | 2018 |
| Locomotion selection strategy for multi-locomotion robot based on stability and efficiency T Kobayashi, T Aoyama, M Sobajima, K Sekiyama, T Fukuda 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems …, 2013 | 21 | 2013 |
| Whole‐Body Multicontact Haptic Human–Humanoid Interaction Based on Leader–Follower Switching: A Robot Dance of the “Box Step” T Kobayashi, E Dean-Leon, JR Guadarrama-Olvera, F Bergner, G Cheng Advanced Intelligent Systems 4 (2), 2100038, 2022 | 20 | 2022 |
| Selection algorithm for locomotion based on the evaluation of falling risk T Kobayashi, T Aoyama, K Sekiyama, T Fukuda IEEE Transactions on Robotics 31 (3), 750-765, 2015 | 19 | 2015 |
| Adaptive speed controller using swing leg motion for 3-D limit-cycle-based bipedal gait T Kobayashi, T Aoyama, Y Hasegawa, K Sekiyama, T Fukuda Nonlinear Dynamics 84, 2285-2304, 2016 | 18 | 2016 |
| Bottom-up multi-agent reinforcement learning by reward shaping for cooperative-competitive tasks T Aotani, T Kobayashi, K Sugimoto Applied Intelligence 51 (7), 4434-4452, 2021 | 16 | 2021 |
| Proximal policy optimization with relative pearson divergence T Kobayashi 2021 IEEE International Conference on Robotics and Automation (ICRA), 8416-8421, 2021 | 15 | 2021 |
| Continual learning exploiting structure of fractal reservoir computing T Kobayashi, T Sugino Artificial Neural Networks and Machine Learning–ICANN 2019: Workshop and …, 2019 | 12 | 2019 |
| Adaterm: Adaptive t-distribution estimated robust moments towards noise-robust stochastic gradient optimizer WEL Ilboudo, T Kobayashi, T Matsubara Available at SSRN 4349092, 2022 | 11 | 2022 |
| Meta-optimization of bias-variance trade-off in stochastic model learning T Aotani, T Kobayashi, K Sugimoto IEEE Access 9, 148783-148799, 2021 | 11 | 2021 |
| Towards deep robot learning with optimizer applicable to non-stationary problems T Kobayashi 2021 IEEE/SICE International Symposium on System Integration (SII), 190-194, 2021 | 10 | 2021 |
| q-VAE for disentangled representation learning and latent dynamical systems T Kobayashis IEEE Robotics and Automation Letters 5 (4), 5669-5676, 2020 | 10 | 2020 |
| Optimistic reinforcement learning by forward Kullback–Leibler divergence optimization T Kobayashi Neural Networks 152, 169-180, 2022 | 9 | 2022 |
| Reduction of noise and vibration in drum type washing machine using Q-learning T Shimizu, H Funakoshi, T Kobayashi, K Sugimoto Control Engineering Practice 122, 105095, 2022 | 9 | 2022 |
| Adaptive and multiple time-scale eligibility traces for online deep reinforcement learning T Kobayashi Robotics and Autonomous Systems 151, 104019, 2022 | 9 | 2022 |
| Reinforcement learning for quadrupedal locomotion with design of continual–hierarchical curriculum T Kobayashi, T Sugino Engineering Applications of Artificial Intelligence 95, 103869, 2020 | 9 | 2020 |
| Tadam: A robust stochastic gradient optimizer WEL Ilboudo, T Kobayashi, K Sugimoto arXiv preprint arXiv:2003.00179, 2020 | 9 | 2020 |
Aoyama TadayoshiNagoya University, Hiroshima University在 mae.nagoya-u.ac.jp 的电子邮件经过验证
